In an enterprise having computer network servers that act as web servers or provide web services and/or web applications and so forth, multiple “target” servers are often arranged in a cluster so that the load from incoming requests and the like can be distributed among multiple machines. One network load balancing solution provides a single-tier, front-end server model, but this is relatively expensive, requiring the enterprise to purchase a high-end server or hardware load balancer to operate in front of the cluster.
Network load balancing solutions that move away from the single-tier, front-end server model typically do so by distributing the load balancing problem across the cluster of target servers themselves. This model is cheaper than the front-end server model, and it can generally be deployed using commodity switches and special software sitting on each target server. This typical distributed software-based solution has its own drawbacks, however, such as the lack of a single point for aggregating dynamic data about the cluster. The inability to gather and analyze such data makes it difficult for the cluster to dynamically adjust the distribution of load. For example, a server may become overloaded due to other applications it is hosting, or it may be underutilized if it only recently joined the cluster. In either event, a transfer of load to or from this server may be warranted, however there is no simple or standard solution to this problem in a software-based distributed load balancing system.